{"title":"Artificial intelligence-based droplet size prediction for microfluidic system","authors":"Sameer Dubey, Pradeep Vishwakarma, TVS Ramarao, Satish Kumar Dubey, Sanket Goel, Arshad Javed","doi":"10.1108/hff-07-2023-0361","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and <em>R</em><sup>2</sup> = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.</p><!--/ Abstract__block -->","PeriodicalId":14263,"journal":{"name":"International Journal of Numerical Methods for Heat & Fluid Flow","volume":"21 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Numerical Methods for Heat & Fluid Flow","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/hff-07-2023-0361","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.
Design/methodology/approach
The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.
Findings
Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R2 = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.
Originality/value
Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.
期刊介绍:
The main objective of this international journal is to provide applied mathematicians, engineers and scientists engaged in computer-aided design and research in computational heat transfer and fluid dynamics, whether in academic institutions of industry, with timely and accessible information on the development, refinement and application of computer-based numerical techniques for solving problems in heat and fluid flow. - See more at: http://emeraldgrouppublishing.com/products/journals/journals.htm?id=hff#sthash.Kf80GRt8.dpuf